Improving Quantitative Rainfall Prediction Using Ensemble Analogues in the Tropics: Case Study of Uganda

Abstract

Accurate and timely rainfall prediction enhances productivity and can aid proper planning in sectors such as agriculture, health, transport and water resources. However quantitative rainfall prediction is normally a challenge and for this reason, this study was conducted with an aim of improving rainfall prediction using ensemble methods. It first assessed the performance of six convective schemes (Kain–Fritsch (KF); Betts–Miller–Janjić (BMJ); Grell–Fretas (GF); Grell 3D ensemble (G3); New–Tiedke (NT) and Grell–Devenyi (GD)) using the root mean square error (RMSE) and mean error (ME) focusing on the March–May 2013 rainfall period over Uganda. 18 ensemble members were then generated from the three best performing convective schemes (i.e., KF, GF and G3). The daily rainfall predicted by the three ensemble methods (i.e., ensemble mean (ENS); ensemble mean analogue (EMA) and multi–member analogue ensemble (MAEM)) was then compared with the observed daily rainfall and the RMSE and ME computed. The results shows that the ENS presented a smaller RMSE compared to individual schemes (ENS: 10.02; KF: 23.96; BMJ: 26.04; GF: 25.85; G3: 24.07; NT: 29.13 and GD: 26.27) and a better bias (ENS: −1.28; KF: −1.62; BMJ: −4.04; GF: −3.90; G3: −3.62; NT: −5.41 and GD: −4.07). The EMA and MAEM presented 13 out of 21 stations and 17 out of 21 stations respectively with smaller RMSE compared to ENS thus demonstrating additional improvement in predictive performance. This study proposed and described MAEM and found it producing comparatively better quantitative rainfall prediction performance compared to the other ensemble methods used. The MAEM method should be valid regardless the nature of the rainfall season.

An Investigation on Spatiotemporal Variability of Drought Events under Different Climate Change Scenarios, Case Study: Rakai District

“…The IDW is used in many studies that present spatially varying patterns e.g. [31] who used the IDW in modeling the spatial and temporal patterns of precipitation in Northwestern Uganda.…”

Examining the Impact of Bias Correction on the Prediction Skill of Regional Climate Projections

“…Uganda is located within the latitudes: 1˚29'S to 4˚12'N and the longitudes: 29˚34'E to 35˚29'E [9] [10]. It is a landlocked country found in the eastern part of equatorial Africa [10] [12]. The country is relatively flat in the central with a few highland areas; and having mountains in the East (Mt.…”

Analog‐based post‐processing of the ALADIN‐LAEF ensemble predictions in complex terrain

“…Mugume et al . (), who used the analog approach to post‐process ensemble members who use different convection parametrization schemes, also explored the same idea. The authors demonstrated a root‐mean‐square error (RMSE) and bias reduction in rainfall prediction when using corresponding predictions of the (starting) ensemble mean analog as a forecast.…”

“…Following the work of Eckel and Delle Monache () and Mugume et al . (), the main goal of this study is to significantly improve the ALADIN‐LAEF ensemble 10 m wind speed forecast while maintaining low computational cost for the analog search. To test the performance of the analog‐based post‐processing and determine the optimal configuration, several experiments using different sources of information available to the ALADIN‐LAEF ensemble forecasts are performed.…”

“…The experiments include using one or more ALADIN‐LAEF meteorological variables as predictors. Through performed analysis, the experiments including only information about the ALADIN‐LAEF ensemble mean (as suggested by Hamill and Whitaker, ) or every ensemble member (similar to Mugume et al ., ) are also tested. A novelty in this study is the usage of the starting model ensemble uncertainty through its standard deviation ( σ ) in addition to ensemble mean ( μ ).…”